diff --git a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py
index b510ff97ac..9d4cb89bba 100644
--- a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py
+++ b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py
@@ -327,8 +327,8 @@ def _reset(self):
             del self.data_range_
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "feature_range",
             "copy"
         ]
@@ -652,8 +652,9 @@ def _reset(self):
             del self.mean_
             del self.var_
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "with_mean",
             "with_std",
             "copy"
@@ -956,8 +957,9 @@ def _reset(self):
             del self.n_samples_seen_
             del self.max_abs_
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "copy"
         ]
 
@@ -1206,8 +1208,9 @@ def __init__(self, *, with_centering=True, with_scaling=True,
         self.quantile_range = quantile_range
         self.copy = copy
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "with_centering",
             "with_scaling",
             "quantile_range",
@@ -1479,8 +1482,9 @@ def __init__(self, degree=2, *, interaction_only=False, include_bias=True,
         self.include_bias = include_bias
         self.order = order
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "degree",
             "interaction_only",
             "include_bias",
@@ -2274,8 +2278,9 @@ def __init__(self, *, n_quantiles=1000, output_distribution='uniform',
         self.random_state = random_state
         self.copy = copy
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "n_quantiles",
             "output_distribution",
             "ignore_implicit_zeros",
@@ -2798,8 +2803,9 @@ def __init__(self, method='yeo-johnson', *, standardize=True, copy=True):
         self.standardize = standardize
         self.copy = copy
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "method",
             "standardize",
             "copy"
diff --git a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_discretization.py b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_discretization.py
index 5bafc40677..6bf6390fcf 100644
--- a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_discretization.py
+++ b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_discretization.py
@@ -159,8 +159,8 @@ def __init__(self, n_bins=5, *, encode='onehot', strategy='quantile'):
         self.strategy = strategy
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "n_bins",
             "encode",
             "strategy"
diff --git a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_imputation.py b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_imputation.py
index 8074ee4b54..2be0c81ff0 100644
--- a/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_imputation.py
+++ b/python/cuml/cuml/_thirdparty/sklearn/preprocessing/_imputation.py
@@ -244,8 +244,8 @@ def __init__(self, *, missing_values=np.nan, strategy="mean",
         self.copy = copy
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "strategy",
             "fill_value",
             "verbose",
@@ -545,8 +545,9 @@ def __init__(self, *, missing_values=np.nan, features="missing-only",
         self.sparse = sparse
         self.error_on_new = error_on_new
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "missing_values",
             "features",
             "sparse",
diff --git a/python/cuml/cuml/cluster/agglomerative.pyx b/python/cuml/cuml/cluster/agglomerative.pyx
index cc2fd1021d..790db73362 100644
--- a/python/cuml/cuml/cluster/agglomerative.pyx
+++ b/python/cuml/cuml/cluster/agglomerative.pyx
@@ -280,8 +280,8 @@ class AgglomerativeClustering(Base, ClusterMixin, CMajorInputTagMixin):
         return self.fit(X).labels_
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "n_clusters",
             "affinity",
             "metric",
diff --git a/python/cuml/cuml/cluster/dbscan.pyx b/python/cuml/cuml/cluster/dbscan.pyx
index 139be36829..9480ccccf6 100644
--- a/python/cuml/cuml/cluster/dbscan.pyx
+++ b/python/cuml/cuml/cluster/dbscan.pyx
@@ -467,8 +467,8 @@ class DBSCAN(UniversalBase,
         return self.labels_
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "eps",
             "min_samples",
             "max_mbytes_per_batch",
diff --git a/python/cuml/cuml/cluster/hdbscan/hdbscan.pyx b/python/cuml/cuml/cluster/hdbscan/hdbscan.pyx
index e27f54cc85..0078b145c7 100644
--- a/python/cuml/cuml/cluster/hdbscan/hdbscan.pyx
+++ b/python/cuml/cuml/cluster/hdbscan/hdbscan.pyx
@@ -1113,8 +1113,8 @@ class HDBSCAN(UniversalBase, ClusterMixin, CMajorInputTagMixin):
             self._cpu_to_gpu_interop_prepped = True
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "metric",
             "min_cluster_size",
             "max_cluster_size",
diff --git a/python/cuml/cuml/cluster/kmeans.pyx b/python/cuml/cuml/cluster/kmeans.pyx
index a2de8da9a3..9ba1cb710a 100644
--- a/python/cuml/cuml/cluster/kmeans.pyx
+++ b/python/cuml/cuml/cluster/kmeans.pyx
@@ -697,8 +697,8 @@ class KMeans(UniversalBase,
         return self.transform(X, convert_dtype=convert_dtype)
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ['n_init', 'oversampling_factor', 'max_samples_per_batch',
                 'init', 'max_iter', 'n_clusters', 'random_state',
                 'tol', "convert_dtype"]
diff --git a/python/cuml/cuml/dask/cluster/dbscan.py b/python/cuml/cuml/dask/cluster/dbscan.py
index 51c22abca6..b71e34682a 100644
--- a/python/cuml/cuml/dask/cluster/dbscan.py
+++ b/python/cuml/cuml/dask/cluster/dbscan.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -160,5 +160,5 @@ def fit_predict(self, X, out_dtype="int32"):
         self.fit(X, out_dtype)
         return self.get_combined_model().labels_
 
-    def get_param_names(self):
+    def _get_param_names(self):
         return list(self.kwargs.keys())
diff --git a/python/cuml/cuml/dask/cluster/kmeans.py b/python/cuml/cuml/dask/cluster/kmeans.py
index b014dc4fb2..3dfeced41f 100644
--- a/python/cuml/cuml/dask/cluster/kmeans.py
+++ b/python/cuml/cuml/dask/cluster/kmeans.py
@@ -302,5 +302,5 @@ def score(self, X, sample_weight=None):
             cp.asarray(self.client.compute(scores, sync=True)) * -1.0
         )
 
-    def get_param_names(self):
+    def _get_param_names(self):
         return list(self.kwargs.keys())
diff --git a/python/cuml/cuml/dask/decomposition/pca.py b/python/cuml/cuml/dask/decomposition/pca.py
index 896eb58606..8cebb2764f 100644
--- a/python/cuml/cuml/dask/decomposition/pca.py
+++ b/python/cuml/cuml/dask/decomposition/pca.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -215,7 +215,7 @@ def inverse_transform(self, X, delayed=True):
         """
         return self._inverse_transform(X, n_dims=2, delayed=delayed)
 
-    def get_param_names(self):
+    def _get_param_names(self):
         return list(self.kwargs.keys())
 
     @staticmethod
diff --git a/python/cuml/cuml/dask/decomposition/tsvd.py b/python/cuml/cuml/dask/decomposition/tsvd.py
index d76d08bda2..67392b7555 100644
--- a/python/cuml/cuml/dask/decomposition/tsvd.py
+++ b/python/cuml/cuml/dask/decomposition/tsvd.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -177,7 +177,7 @@ def inverse_transform(self, X, delayed=True):
         """
         return self._inverse_transform(X, n_dims=2, delayed=delayed)
 
-    def get_param_names(self):
+    def _get_param_names(self):
         return list(self.kwargs.keys())
 
     @staticmethod
diff --git a/python/cuml/cuml/dask/linear_model/linear_regression.py b/python/cuml/cuml/dask/linear_model/linear_regression.py
index f8c5dfd7b0..98ffba672d 100644
--- a/python/cuml/cuml/dask/linear_model/linear_regression.py
+++ b/python/cuml/cuml/dask/linear_model/linear_regression.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -106,7 +106,7 @@ def predict(self, X, delayed=True):
         """
         return self._predict(X, delayed=delayed)
 
-    def get_param_names(self):
+    def _get_param_names(self):
         return list(self.kwargs.keys())
 
     @staticmethod
diff --git a/python/cuml/cuml/dask/linear_model/ridge.py b/python/cuml/cuml/dask/linear_model/ridge.py
index 32db990979..2830f3ce38 100644
--- a/python/cuml/cuml/dask/linear_model/ridge.py
+++ b/python/cuml/cuml/dask/linear_model/ridge.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -114,7 +114,7 @@ def predict(self, X, delayed=True):
         """
         return self._predict(X, delayed=delayed)
 
-    def get_param_names(self):
+    def _get_param_names(self):
         return list(self.kwargs.keys())
 
     @staticmethod
diff --git a/python/cuml/cuml/decomposition/incremental_pca.py b/python/cuml/cuml/decomposition/incremental_pca.py
index 70663ceeb8..925219d0bc 100644
--- a/python/cuml/cuml/decomposition/incremental_pca.py
+++ b/python/cuml/cuml/decomposition/incremental_pca.py
@@ -449,9 +449,9 @@ def transform(self, X, convert_dtype=False) -> CumlArray:
             return super().transform(X)
 
     @classmethod
-    def get_param_names(cls):
+    def _get_param_names(cls):
         # Skip super() since we dont pass any extra parameters in __init__
-        return Base.get_param_names() + [
+        return Base._get_param_names() + [
             "n_components",
             "whiten",
             "copy",
diff --git a/python/cuml/cuml/decomposition/pca.pyx b/python/cuml/cuml/decomposition/pca.pyx
index 08f8f2ec6e..9433f724b9 100644
--- a/python/cuml/cuml/decomposition/pca.pyx
+++ b/python/cuml/cuml/decomposition/pca.pyx
@@ -726,8 +726,8 @@ class PCA(UniversalBase,
             return t_input_data
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ["copy", "iterated_power", "n_components", "svd_solver", "tol",
                 "whiten", "random_state"]
 
diff --git a/python/cuml/cuml/decomposition/tsvd.pyx b/python/cuml/cuml/decomposition/tsvd.pyx
index dd8dfe0282..55caa84c9b 100644
--- a/python/cuml/cuml/decomposition/tsvd.pyx
+++ b/python/cuml/cuml/decomposition/tsvd.pyx
@@ -483,8 +483,8 @@ class TruncatedSVD(UniversalBase,
             return t_input_data
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ["algorithm", "n_components", "n_iter", "random_state", "tol"]
 
     def get_attr_names(self):
diff --git a/python/cuml/cuml/ensemble/randomforest_common.pyx b/python/cuml/cuml/ensemble/randomforest_common.pyx
index a6dd918879..38c15eaca2 100644
--- a/python/cuml/cuml/ensemble/randomforest_common.pyx
+++ b/python/cuml/cuml/ensemble/randomforest_common.pyx
@@ -383,8 +383,8 @@ class BaseRandomForestModel(Base):
         return preds
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + BaseRandomForestModel._param_names
+    def _get_param_names(cls):
+        return super()._get_param_names() + BaseRandomForestModel._param_names
 
     def set_params(self, **params):
         self.treelite_serialized_model = None
diff --git a/python/cuml/cuml/experimental/linear_model/lars.pyx b/python/cuml/cuml/experimental/linear_model/lars.pyx
index 6a81b0bc23..4a836740c7 100644
--- a/python/cuml/cuml/experimental/linear_model/lars.pyx
+++ b/python/cuml/cuml/experimental/linear_model/lars.pyx
@@ -398,7 +398,7 @@ class Lars(Base, RegressorMixin):
         return preds
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ['copy_X', 'fit_intercept', 'fit_path', 'n_nonzero_coefs',
              'normalize', 'precompute', 'eps']
diff --git a/python/cuml/cuml/feature_extraction/_tfidf.py b/python/cuml/cuml/feature_extraction/_tfidf.py
index c4d7a5fd1b..2cf5974119 100644
--- a/python/cuml/cuml/feature_extraction/_tfidf.py
+++ b/python/cuml/cuml/feature_extraction/_tfidf.py
@@ -302,8 +302,8 @@ def idf_(self, value):
         )
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "norm",
             "use_idf",
             "smooth_idf",
diff --git a/python/cuml/cuml/internals/base.pyx b/python/cuml/cuml/internals/base.pyx
index ad6f761799..9813acbba4 100644
--- a/python/cuml/cuml/internals/base.pyx
+++ b/python/cuml/cuml/internals/base.pyx
@@ -183,7 +183,7 @@ class Base(TagsMixin,
                 # inference logic goes here
 
             @classmethod
-            def get_param_names(cls):
+            def _get_param_names(cls):
                 # return a list of hyperparam names supported by this algo
 
         # stream and handle example:
@@ -271,7 +271,8 @@ class Base(TagsMixin,
             output += ' <sk_model_ attribute used>'
         return output
 
-    def get_param_names(self):
+    @classmethod
+    def _get_param_names(cls):
         """
         Returns a list of hyperparameter names owned by this class. It is
         expected that every child class overrides this method and appends its
@@ -283,12 +284,12 @@ class Base(TagsMixin,
     def get_params(self, deep=True):
         """
         Returns a dict of all params owned by this class. If the child class
-        has appropriately overridden the `get_param_names` method and does not
+        has appropriately overridden the `_get_param_names` method and does not
         need anything other than what is there in this method, then it doesn't
         have to override this method
         """
         params = dict()
-        variables = self.get_param_names()
+        variables = self._get_param_names()
         for key in variables:
             var_value = getattr(self, key, None)
             params[key] = var_value
@@ -298,12 +299,12 @@ class Base(TagsMixin,
         """
         Accepts a dict of params and updates the corresponding ones owned by
         this class. If the child class has appropriately overridden the
-        `get_param_names` method and does not need anything other than what is,
+        `_get_param_names` method and does not need anything other than what is,
         there in this method, then it doesn't have to override this method
         """
         if not params:
             return self
-        variables = self.get_param_names()
+        variables = self._get_param_names()
         for key, value in params.items():
             if key not in variables:
                 raise ValueError("Bad param '%s' passed to set_params" % key)
diff --git a/python/cuml/cuml/kernel_ridge/kernel_ridge.pyx b/python/cuml/cuml/kernel_ridge/kernel_ridge.pyx
index 98d1bd98de..6063f27c99 100644
--- a/python/cuml/cuml/kernel_ridge/kernel_ridge.pyx
+++ b/python/cuml/cuml/kernel_ridge/kernel_ridge.pyx
@@ -227,8 +227,8 @@ class KernelRidge(Base, RegressorMixin):
         self.kernel_params = kernel_params
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "alpha",
             "kernel",
             "gamma",
diff --git a/python/cuml/cuml/linear_model/elastic_net.pyx b/python/cuml/cuml/linear_model/elastic_net.pyx
index cae04051e1..a8e6b75a3d 100644
--- a/python/cuml/cuml/linear_model/elastic_net.pyx
+++ b/python/cuml/cuml/linear_model/elastic_net.pyx
@@ -273,8 +273,8 @@ class ElasticNet(UniversalBase,
         return self
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "alpha",
             "l1_ratio",
             "fit_intercept",
diff --git a/python/cuml/cuml/linear_model/lasso.py b/python/cuml/cuml/linear_model/lasso.py
index 9b0aafa5ef..65a30be791 100644
--- a/python/cuml/cuml/linear_model/lasso.py
+++ b/python/cuml/cuml/linear_model/lasso.py
@@ -166,5 +166,5 @@ def __init__(
         )
 
     @classmethod
-    def get_param_names(cls):
-        return list(set(super().get_param_names()) - {"l1_ratio"})
+    def _get_param_names(cls):
+        return list(set(super()._get_param_names()) - {"l1_ratio"})
diff --git a/python/cuml/cuml/linear_model/linear_regression.pyx b/python/cuml/cuml/linear_model/linear_regression.pyx
index ba38ba1711..35a73c111f 100644
--- a/python/cuml/cuml/linear_model/linear_regression.pyx
+++ b/python/cuml/cuml/linear_model/linear_regression.pyx
@@ -492,8 +492,8 @@ class LinearRegression(LinearPredictMixin,
         return super()._predict(X, convert_dtype=convert_dtype)
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ['algorithm', 'fit_intercept', 'copy_X', 'normalize']
 
     def get_attr_names(self):
diff --git a/python/cuml/cuml/linear_model/logistic_regression.pyx b/python/cuml/cuml/linear_model/logistic_regression.pyx
index 1d08d2f102..aa5283fef7 100644
--- a/python/cuml/cuml/linear_model/logistic_regression.pyx
+++ b/python/cuml/cuml/linear_model/logistic_regression.pyx
@@ -535,8 +535,8 @@ class LogisticRegression(UniversalBase,
         self.solver_model.intercept_ = value
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "penalty",
             "tol",
             "C",
diff --git a/python/cuml/cuml/linear_model/mbsgd_classifier.pyx b/python/cuml/cuml/linear_model/mbsgd_classifier.pyx
index 6f13288f8d..3a7fcc772e 100644
--- a/python/cuml/cuml/linear_model/mbsgd_classifier.pyx
+++ b/python/cuml/cuml/linear_model/mbsgd_classifier.pyx
@@ -214,8 +214,8 @@ class MBSGDClassifier(Base,
         return self
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "loss",
             "penalty",
             "alpha",
diff --git a/python/cuml/cuml/linear_model/mbsgd_regressor.pyx b/python/cuml/cuml/linear_model/mbsgd_regressor.pyx
index 1a2a4b52b8..a738eb6d74 100644
--- a/python/cuml/cuml/linear_model/mbsgd_regressor.pyx
+++ b/python/cuml/cuml/linear_model/mbsgd_regressor.pyx
@@ -208,8 +208,8 @@ class MBSGDRegressor(Base,
         return self
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "loss",
             "penalty",
             "alpha",
diff --git a/python/cuml/cuml/linear_model/ridge.pyx b/python/cuml/cuml/linear_model/ridge.pyx
index 92a94ddf9e..ae84f1002a 100644
--- a/python/cuml/cuml/linear_model/ridge.pyx
+++ b/python/cuml/cuml/linear_model/ridge.pyx
@@ -357,8 +357,8 @@ class Ridge(UniversalBase,
         return self
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ['solver', 'fit_intercept', 'normalize', 'alpha']
 
     def get_attr_names(self):
diff --git a/python/cuml/cuml/manifold/t_sne.pyx b/python/cuml/cuml/manifold/t_sne.pyx
index 7d608167a1..11ade2ffe2 100644
--- a/python/cuml/cuml/manifold/t_sne.pyx
+++ b/python/cuml/cuml/manifold/t_sne.pyx
@@ -691,8 +691,8 @@ class TSNE(UniversalBase,
         return state
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "n_components",
             "perplexity",
             "early_exaggeration",
diff --git a/python/cuml/cuml/manifold/umap.pyx b/python/cuml/cuml/manifold/umap.pyx
index dc5f037467..c873461a95 100644
--- a/python/cuml/cuml/manifold/umap.pyx
+++ b/python/cuml/cuml/manifold/umap.pyx
@@ -910,8 +910,8 @@ class UMAP(UniversalBase,
         super().gpu_to_cpu()
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "n_neighbors",
             "n_components",
             "n_epochs",
diff --git a/python/cuml/cuml/multiclass/multiclass.py b/python/cuml/cuml/multiclass/multiclass.py
index 45671a275b..61a79e1d31 100644
--- a/python/cuml/cuml/multiclass/multiclass.py
+++ b/python/cuml/cuml/multiclass/multiclass.py
@@ -193,8 +193,8 @@ def decision_function(self, X) -> CumlArray:
             return self.multiclass_estimator.decision_function(X)
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + ["estimator", "strategy"]
+    def _get_param_names(cls):
+        return super()._get_param_names() + ["estimator", "strategy"]
 
 
 class OneVsRestClassifier(MulticlassClassifier):
@@ -266,8 +266,9 @@ def __init__(
             strategy="ovr",
         )
 
-    def get_param_names(self):
-        param_names = super().get_param_names()
+    @classmethod
+    def _get_param_names(cls):
+        param_names = super()._get_param_names()
         param_names.remove("strategy")
         return param_names
 
@@ -340,7 +341,8 @@ def __init__(
             strategy="ovo",
         )
 
-    def get_param_names(self):
-        param_names = super().get_param_names()
+    @classmethod
+    def _get_param_names(cls):
+        param_names = super()._get_param_names()
         param_names.remove("strategy")
         return param_names
diff --git a/python/cuml/cuml/naive_bayes/naive_bayes.py b/python/cuml/cuml/naive_bayes/naive_bayes.py
index b393090ea6..701f88862e 100644
--- a/python/cuml/cuml/naive_bayes/naive_bayes.py
+++ b/python/cuml/cuml/naive_bayes/naive_bayes.py
@@ -725,8 +725,8 @@ def _joint_log_likelihood(self, X):
         return cp.array(joint_log_likelihood).T
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + ["priors", "var_smoothing"]
+    def _get_param_names(cls):
+        return super()._get_param_names() + ["priors", "var_smoothing"]
 
 
 class _BaseDiscreteNB(_BaseNB):
@@ -1059,8 +1059,9 @@ def _count_sparse(
         self.feature_count_ = self.feature_count_ + counts
         self.class_count_ = self.class_count_ + class_c
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "alpha",
             "fit_prior",
             "class_prior",
@@ -1374,8 +1375,9 @@ def _update_feature_log_prob(self, alpha):
             smoothed_cc.reshape(-1, 1)
         )
 
-    def get_param_names(self):
-        return super().get_param_names() + ["binarize"]
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + ["binarize"]
 
 
 class ComplementNB(_BaseDiscreteNB):
@@ -1537,8 +1539,9 @@ def _update_feature_log_prob(self, alpha):
             feature_log_prob = -logged
         self.feature_log_prob_ = feature_log_prob
 
-    def get_param_names(self):
-        return super().get_param_names() + ["norm"]
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + ["norm"]
 
 
 class CategoricalNB(_BaseDiscreteNB):
diff --git a/python/cuml/cuml/neighbors/kernel_density.py b/python/cuml/cuml/neighbors/kernel_density.py
index ceb88e91b2..3af2107995 100644
--- a/python/cuml/cuml/neighbors/kernel_density.py
+++ b/python/cuml/cuml/neighbors/kernel_density.py
@@ -226,8 +226,8 @@ def __init__(
             raise ValueError("invalid kernel: '{0}'".format(kernel))
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "bandwidth",
             "kernel",
             "metric",
diff --git a/python/cuml/cuml/neighbors/kneighbors_classifier.pyx b/python/cuml/cuml/neighbors/kneighbors_classifier.pyx
index 0751bf4c5c..ed72909203 100644
--- a/python/cuml/cuml/neighbors/kneighbors_classifier.pyx
+++ b/python/cuml/cuml/neighbors/kneighbors_classifier.pyx
@@ -299,5 +299,5 @@ class KNeighborsClassifier(ClassifierMixin,
             if len(final_classes) == 1 else tuple(final_classes)
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + ["weights"]
+    def _get_param_names(cls):
+        return super()._get_param_names() + ["weights"]
diff --git a/python/cuml/cuml/neighbors/kneighbors_regressor.pyx b/python/cuml/cuml/neighbors/kneighbors_regressor.pyx
index c9ec8f1b93..75fcfce5c6 100644
--- a/python/cuml/cuml/neighbors/kneighbors_regressor.pyx
+++ b/python/cuml/cuml/neighbors/kneighbors_regressor.pyx
@@ -235,5 +235,5 @@ class KNeighborsRegressor(RegressorMixin,
         return results
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + ["weights"]
+    def _get_param_names(cls):
+        return super()._get_param_names() + ["weights"]
diff --git a/python/cuml/cuml/neighbors/nearest_neighbors.pyx b/python/cuml/cuml/neighbors/nearest_neighbors.pyx
index 0c0ebb09a3..202d65ca8b 100644
--- a/python/cuml/cuml/neighbors/nearest_neighbors.pyx
+++ b/python/cuml/cuml/neighbors/nearest_neighbors.pyx
@@ -421,8 +421,8 @@ class NearestNeighbors(UniversalBase,
         return self
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + \
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ["n_neighbors", "algorithm", "metric",
                 "p", "metric_params", "algo_params", "n_jobs"]
 
diff --git a/python/cuml/cuml/preprocessing/LabelEncoder.py b/python/cuml/cuml/preprocessing/LabelEncoder.py
index ba6c1975ab..960935e61f 100644
--- a/python/cuml/cuml/preprocessing/LabelEncoder.py
+++ b/python/cuml/cuml/preprocessing/LabelEncoder.py
@@ -285,7 +285,7 @@ def inverse_transform(self, y: cudf.Series) -> cudf.Series:
         return res
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "handle_unknown",
         ]
diff --git a/python/cuml/cuml/preprocessing/TargetEncoder.py b/python/cuml/cuml/preprocessing/TargetEncoder.py
index d0df2fbf66..a6e7524cf0 100644
--- a/python/cuml/cuml/preprocessing/TargetEncoder.py
+++ b/python/cuml/cuml/preprocessing/TargetEncoder.py
@@ -509,7 +509,7 @@ def _get_output_type(self, x):
         return "cupy"
 
     @classmethod
-    def get_param_names(cls):
+    def _get_param_names(cls):
         return [
             "n_folds",
             "smooth",
@@ -522,7 +522,7 @@ def get_params(self, deep=False):
         Returns a dict of all params owned by this class.
         """
         params = dict()
-        variables = self.get_param_names()
+        variables = self._get_param_names()
         for key in variables:
             var_value = getattr(self, key, None)
             params[key] = var_value
diff --git a/python/cuml/cuml/preprocessing/encoders.py b/python/cuml/cuml/preprocessing/encoders.py
index 7874aaf4dc..943f3c294c 100644
--- a/python/cuml/cuml/preprocessing/encoders.py
+++ b/python/cuml/cuml/preprocessing/encoders.py
@@ -603,8 +603,8 @@ def get_feature_names(self, input_features=None):
         return np.array(feature_names, dtype=object)
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "categories",
             "drop",
             "sparse",
@@ -768,8 +768,9 @@ def inverse_transform(self, X):
         r = DataFrame(result)
         return _get_output(self.output_type, self.input_type, r, self.dtype)
 
-    def get_param_names(self):
-        return super().get_param_names() + [
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "categories",
             "dtype",
             "handle_unknown",
diff --git a/python/cuml/cuml/preprocessing/label.py b/python/cuml/cuml/preprocessing/label.py
index 742383188d..20aac36ac8 100644
--- a/python/cuml/cuml/preprocessing/label.py
+++ b/python/cuml/cuml/preprocessing/label.py
@@ -288,8 +288,8 @@ def inverse_transform(self, y, threshold=None) -> CumlArray:
         return invert_labels(y_mapped, self.classes_)
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "neg_label",
             "pos_label",
             "sparse_output",
diff --git a/python/cuml/cuml/random_projection/random_projection.pyx b/python/cuml/cuml/random_projection/random_projection.pyx
index 18c61b606e..81811a4849 100644
--- a/python/cuml/cuml/random_projection/random_projection.pyx
+++ b/python/cuml/cuml/random_projection/random_projection.pyx
@@ -446,8 +446,8 @@ class GaussianRandomProjection(Base,
             random_state=random_state)
 
     @classmethod
-    def get_param_names(cls):
-        return Base.get_param_names() + [
+    def _get_param_names(cls):
+        return Base._get_param_names() + [
             "n_components",
             "eps",
             "random_state"
@@ -590,8 +590,9 @@ class SparseRandomProjection(Base,
             dense_output=dense_output,
             random_state=random_state)
 
-    def get_param_names(self):
-        return Base.get_param_names(self) + [
+    @classmethod
+    def _get_param_names(cls):
+        return Base._get_param_names() + [
             "n_components",
             "density",
             "eps",
diff --git a/python/cuml/cuml/solvers/cd.pyx b/python/cuml/cuml/solvers/cd.pyx
index a6874f7025..ba6c5ac12b 100644
--- a/python/cuml/cuml/solvers/cd.pyx
+++ b/python/cuml/cuml/solvers/cd.pyx
@@ -360,8 +360,8 @@ class CD(Base,
         return preds
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "loss",
             "alpha",
             "l1_ratio",
diff --git a/python/cuml/cuml/solvers/qn.pyx b/python/cuml/cuml/solvers/qn.pyx
index 6d04242ee4..72f51c25b2 100644
--- a/python/cuml/cuml/solvers/qn.pyx
+++ b/python/cuml/cuml/solvers/qn.pyx
@@ -171,7 +171,7 @@ IF GPUBUILD == 1:
             return x
 
         def __init__(self, **kwargs):
-            allowed_keys = set(self.get_param_names())
+            allowed_keys = set(self._get_param_names())
             for key, val in kwargs.items():
                 if key in allowed_keys:
                     setattr(self, key, val)
@@ -183,7 +183,7 @@ IF GPUBUILD == 1:
             self.params[key] = val
 
         @classmethod
-        def get_param_names(cls):
+        def _get_param_names(cls):
             return cls.get_param_defaults().keys()
 
         def __str__(self):
@@ -950,8 +950,9 @@ class QN(Base,
         else:
             self.intercept_ = CumlArray.zeros(shape=_num_classes)
 
-    def get_param_names(self):
-        return super().get_param_names() + \
+    @classmethod
+    def _get_param_names(cls):
+        return super()._get_param_names() + \
             ['loss', 'fit_intercept', 'l1_strength', 'l2_strength',
                 'max_iter', 'tol', 'linesearch_max_iter', 'lbfgs_memory',
                 'warm_start', 'delta', 'penalty_normalized']
diff --git a/python/cuml/cuml/solvers/sgd.pyx b/python/cuml/cuml/solvers/sgd.pyx
index 6e10fab9a8..b6c452cc30 100644
--- a/python/cuml/cuml/solvers/sgd.pyx
+++ b/python/cuml/cuml/solvers/sgd.pyx
@@ -503,8 +503,8 @@ class SGD(Base,
         return preds
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "loss",
             "penalty",
             "alpha",
diff --git a/python/cuml/cuml/svm/linear.pyx b/python/cuml/cuml/svm/linear.pyx
index ff84b33d5b..c5ff47cde9 100644
--- a/python/cuml/cuml/svm/linear.pyx
+++ b/python/cuml/cuml/svm/linear.pyx
@@ -146,13 +146,13 @@ cdef class LSVMPWrapper_:
         self.params[key] = val
 
     def __init__(self, **kwargs):
-        allowed_keys = set(self.get_param_names())
+        allowed_keys = set(self._get_param_names())
         for key, val in kwargs.items():
             if key in allowed_keys:
                 setattr(self, key, val)
 
     @classmethod
-    def get_param_names(cls):
+    def _get_param_names(cls):
         cdef LinearSVMParams ps
         return ps.keys()
 
@@ -213,7 +213,7 @@ def __add_prop(prop_name):
     ))
 
 
-for prop_name in LSVMPWrapper().get_param_names():
+for prop_name in LSVMPWrapper()._get_param_names():
     if not hasattr(LSVMPWrapper, prop_name):
         __add_prop(prop_name)
 del __add_prop
@@ -595,7 +595,7 @@ class LinearSVM(Base, metaclass=WithReexportedParams):
         return state
 
     def __init__(self, **kwargs):
-        # `tol` is special in that it's not present in get_param_names,
+        # `tol` is special in that it's not present in _get_param_names,
         # so having a special logic here does not affect pickling/cloning.
         tol = kwargs.pop('tol', None)
         if tol is not None:
@@ -605,8 +605,8 @@ class LinearSVM(Base, metaclass=WithReexportedParams):
             self.change_tol = tol * default_to_ratio
         # All arguments are optional (they have defaults),
         # yet we need to check for unused arguments
-        allowed_keys = set(self.get_param_names())
-        super_keys = set(super().get_param_names())
+        allowed_keys = set(self._get_param_names())
+        super_keys = set(super()._get_param_names())
         remaining_kwargs = {}
         for key, val in kwargs.items():
             if key not in allowed_keys or key in super_keys:
diff --git a/python/cuml/cuml/svm/linear_svc.py b/python/cuml/cuml/svm/linear_svc.py
index bb53ecb633..40df6f0808 100644
--- a/python/cuml/cuml/svm/linear_svc.py
+++ b/python/cuml/cuml/svm/linear_svc.py
@@ -174,7 +174,7 @@ def loss(self, loss: str):
         self.__loss = loss
 
     @classmethod
-    def get_param_names(cls):
+    def _get_param_names(cls):
         return list(
             {
                 "handle",
@@ -192,7 +192,7 @@ def get_param_names(cls):
                 "grad_tol",
                 "change_tol",
                 "multi_class",
-            }.union(super().get_param_names())
+            }.union(super()._get_param_names())
         )
 
     def fit(self, X, y, sample_weight=None, convert_dtype=True) -> "LinearSVM":
diff --git a/python/cuml/cuml/svm/linear_svr.py b/python/cuml/cuml/svm/linear_svr.py
index 93c2c13389..3f9b8040d9 100644
--- a/python/cuml/cuml/svm/linear_svr.py
+++ b/python/cuml/cuml/svm/linear_svr.py
@@ -153,7 +153,7 @@ def loss(self, loss: str):
         self.__loss = loss
 
     @classmethod
-    def get_param_names(cls):
+    def _get_param_names(cls):
         return list(
             {
                 "handle",
@@ -169,5 +169,5 @@ def get_param_names(cls):
                 "grad_tol",
                 "change_tol",
                 "epsilon",
-            }.union(super().get_param_names())
+            }.union(super()._get_param_names())
         )
diff --git a/python/cuml/cuml/svm/svc.pyx b/python/cuml/cuml/svm/svc.pyx
index d62d4729b0..290f5bc2a2 100644
--- a/python/cuml/cuml/svm/svc.pyx
+++ b/python/cuml/cuml/svm/svc.pyx
@@ -703,8 +703,8 @@ class SVC(SVMBase,
             return super().predict(X, False)
 
     @classmethod
-    def get_param_names(cls):
-        params = super().get_param_names() + \
+    def _get_param_names(cls):
+        params = super()._get_param_names() + \
             ["probability", "random_state", "class_weight",
              "multiclass_strategy"]
 
diff --git a/python/cuml/cuml/svm/svm_base.pyx b/python/cuml/cuml/svm/svm_base.pyx
index bd4f5c5681..9b68147f2b 100644
--- a/python/cuml/cuml/svm/svm_base.pyx
+++ b/python/cuml/cuml/svm/svm_base.pyx
@@ -662,8 +662,8 @@ class SVMBase(Base,
         return preds
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "C",
             "kernel",
             "degree",
diff --git a/python/cuml/cuml/tests/explainer/test_explainer_common.py b/python/cuml/cuml/tests/explainer/test_explainer_common.py
index 5ee4e5f52b..659f8c365e 100644
--- a/python/cuml/cuml/tests/explainer/test_explainer_common.py
+++ b/python/cuml/cuml/tests/explainer/test_explainer_common.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -141,7 +141,7 @@ def test_get_tag_from_model_func(model):
 
     for tag in _default_tags:
         res = get_tag_from_model_func(
-            func=mod.get_param_names, tag=tag, default="FFF"
+            func=mod._get_param_names, tag=tag, default="FFF"
         )
 
         if tag != "preferred_input_order":
@@ -153,7 +153,7 @@ def test_get_handle_from_cuml_model_func(model):
     mod = create_dummy_model(model)
 
     handle = get_handle_from_cuml_model_func(
-        mod.get_param_names, create_new=True
+        mod._get_param_names, create_new=True
     )
 
     assert isinstance(handle, Handle)
diff --git a/python/cuml/cuml/tests/test_base.py b/python/cuml/cuml/tests/test_base.py
index dc70c8cf22..0cd01acabb 100644
--- a/python/cuml/cuml/tests/test_base.py
+++ b/python/cuml/cuml/tests/test_base.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -36,7 +36,7 @@ def test_base_class_usage():
     # Ensure base class returns the 3 main properties needed by all classes
     base = cuml.Base()
     base.handle.sync()
-    base_params = base.get_param_names()
+    base_params = base._get_param_names()
 
     assert "handle" in base_params
     assert "verbose" in base_params
@@ -160,10 +160,10 @@ def get_param_doc(param_doc_obj, name: str):
 @pytest.mark.parametrize("child_class", list(all_base_children.keys()))
 # ignore ColumnTransformer init warning
 @pytest.mark.filterwarnings("ignore:Transformers are required")
-def test_base_children_get_param_names(child_class: str):
+def test_base_children__get_param_names(child_class: str):
     """
     This test ensures that the arguments in `Base.__init__` are available in
-    all derived classes `get_param_names`
+    all derived classes `_get_param_names`
     """
 
     klass = all_base_children[child_class]
@@ -183,9 +183,9 @@ def test_base_children_get_param_names(child_class: str):
         # Create an instance
         obj = klass(*bound.args, **bound.kwargs)
 
-        param_names = obj.get_param_names()
+        param_names = obj._get_param_names()
 
-        # Now ensure the base parameters are included in get_param_names
+        # Now ensure the base parameters are included in _get_param_names
         for name, param in sig.parameters.items():
             if param.name == "output_mem_type":
                 continue  # TODO(wphicks): Add this to all algos
diff --git a/python/cuml/cuml/tsa/arima.pyx b/python/cuml/cuml/tsa/arima.pyx
index e93f871cb2..3513362013 100644
--- a/python/cuml/cuml/tsa/arima.pyx
+++ b/python/cuml/cuml/tsa/arima.pyx
@@ -578,13 +578,10 @@ class ARIMA(Base):
                 setattr(self, "{}_".format(param_name), array)
 
     @classmethod
-    def get_param_names(cls):
-        raise NotImplementedError
-
-    def get_param_names(self):
+    def _get_param_names(cls):
         """
         .. warning:: ARIMA is unable to be cloned at this time.
-            The methods: `get_param_names()`, `get_params` and
+            The methods: `_get_param_names()`, `get_params` and
             `set_params` will raise ``NotImplementedError``
         """
         raise NotImplementedError("ARIMA is unable to be cloned via "
@@ -593,7 +590,7 @@ class ARIMA(Base):
     def get_params(self, deep=True):
         """
         .. warning:: ARIMA is unable to be cloned at this time.
-            The methods: `get_param_names()`, `get_params` and
+            The methods: `_get_param_names()`, `get_params` and
             `set_params` will raise ``NotImplementedError``
         """
         raise NotImplementedError("ARIMA is unable to be cloned via "
@@ -602,7 +599,7 @@ class ARIMA(Base):
     def set_params(self, **params):
         """
         .. warning:: ARIMA is unable to be cloned at this time.
-            The methods: `get_param_names()`, `get_params` and
+            The methods: `_get_param_names()`, `get_params` and
             `set_params` will raise ``NotImplementedError``
         """
         raise NotImplementedError("ARIMA is unable to be cloned via "
diff --git a/python/cuml/cuml/tsa/holtwinters.pyx b/python/cuml/cuml/tsa/holtwinters.pyx
index f22f363051..685e92fdea 100644
--- a/python/cuml/cuml/tsa/holtwinters.pyx
+++ b/python/cuml/cuml/tsa/holtwinters.pyx
@@ -577,8 +577,8 @@ class ExponentialSmoothing(Base):
             raise ValueError("Fit() the model to get season values")
 
     @classmethod
-    def get_param_names(cls):
-        return super().get_param_names() + [
+    def _get_param_names(cls):
+        return super()._get_param_names() + [
             "endog",
             "seasonal",
             "seasonal_periods",
diff --git a/wiki/python/ESTIMATOR_GUIDE.md b/wiki/python/ESTIMATOR_GUIDE.md
index a387df111d..84528608cd 100644
--- a/wiki/python/ESTIMATOR_GUIDE.md
+++ b/wiki/python/ESTIMATOR_GUIDE.md
@@ -15,7 +15,7 @@ This guide is meant to help developers follow the correct patterns when creating
    - [Returning Arrays](#returning-arrays)
 - [Estimator Design](#estimator-design)
    - [Initialization](#initialization)
-   - [Implementing `get_param_names()`](#implementing-get_param_names)
+   - [Implementing `_get_param_names()`](#implementing-_get_param_names)
    - [Estimator Tags and cuML Specific Tags](#estimator-tags-and-cuml-specific-tags)
    - [Estimator Array-Like Attributes](#estimator-array-like-attributes)
    - [Estimator Methods](#estimator-methods)
@@ -77,10 +77,11 @@ At a high level, all cuML Estimators must:
       def predict(self, X) -> CumlArray:
          ...
    ```
-6. Implement `get_param_names()` including values returned by `super().get_param_names()`
+6. Implement `_get_param_names()` including values returned by `super()._get_param_names()`
    ```python
-      def get_param_names(self):
-         return super().get_param_names() + [
+      @classmethod
+   def _get_param_names(cls):
+         return super()._get_param_names() + [
             "eps",
             "min_samples",
          ]
@@ -254,19 +255,20 @@ def __init__(self, my_option="option1"):
 
 This will break cloning since the value of `self.my_option` is not a valid input to `__init__`. Instead, `my_option` should be saved as an attribute as-is.
 
-### Implementing `get_param_names()`
+### Implementing `_get_param_names()`
 
-To support cloning, estimators need to implement the function `get_param_names()`. The returned value should be a list of strings of all estimator attributes that are necessary to duplicate the estimator. This method is used in `Base.get_params()` which will collect the collect the estimator param values from this list and pass this dictionary to a new estimator constructor. Therefore, all strings returned by `get_param_names()` should be arguments in `__init__()` otherwise an invalid argument exception will be raised. Most estimators implement `get_param_names()` similar to:
+To support cloning, estimators need to implement the function `_get_param_names()`. The returned value should be a list of strings of all estimator attributes that are necessary to duplicate the estimator. This method is used in `Base.get_params()` which will collect the collect the estimator param values from this list and pass this dictionary to a new estimator constructor. Therefore, all strings returned by `_get_param_names()` should be arguments in `__init__()` otherwise an invalid argument exception will be raised. Most estimators implement `_get_param_names()` similar to:
 
 ```python
-def get_param_names(self):
-   return super().get_param_names() + [
+@classmethod
+def _get_param_names(cls):
+   return super()._get_param_names() + [
       "eps",
       "min_samples",
    ]
 ```
 
-**Note:** Be sure to include `super().get_param_names()` in the returned list to properly set the `super()` attributes.
+**Note:** Be sure to include `super()._get_param_names()` in the returned list to properly set the `super()` attributes.
 
 ### Estimator Tags and cuML-Specific Tags