diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index d72b0294fa4f4..03a999ffba49e 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -120,6 +120,15 @@ def _handle_zeros_in_scale(scale, copy=True, constant_mask=None):
         return scale
 
 
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "axis": [Options(Integral, {0, 1})],
+        "with_mean": ["boolean"],
+        "with_std": ["boolean"],
+        "copy": ["boolean"],
+    }
+)
 def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True):
     """Standardize a dataset along any axis.
 
@@ -132,7 +141,7 @@ def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True):
     X : {array-like, sparse matrix} of shape (n_samples, n_features)
         The data to center and scale.
 
-    axis : int, default=0
+    axis : {0, 1}, default=0
         Axis used to compute the means and standard deviations along. If 0,
         independently standardize each feature, otherwise (if 1) standardize
         each sample.
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index e127369072828..9581de7630788 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -210,6 +210,7 @@ def _check_function_param_validation(
     "sklearn.metrics.top_k_accuracy_score",
     "sklearn.metrics.zero_one_loss",
     "sklearn.model_selection.train_test_split",
+    "sklearn.preprocessing.scale",
     "sklearn.random_projection.johnson_lindenstrauss_min_dim",
     "sklearn.svm.l1_min_c",
     "sklearn.tree.export_text",