scikit-learn
diff --git a/‎maint_tools/test_docstrings.py
Lines changed: 0 additions & 1 deletion b/‎maint_tools/test_docstrings.py
Lines changed: 0 additions & 1 deletion
diff --git a/‎sklearn/metrics/pairwise.py
Lines changed: 11 additions & 8 deletions b/‎sklearn/metrics/pairwise.py
Lines changed: 11 additions & 8 deletions
@@ -153,7 +153,6 @@
     "sklearn.metrics.pairwise.cosine_distances",
     "sklearn.metrics.pairwise.cosine_similarity",
     "sklearn.metrics.pairwise.distance_metrics",
-    "sklearn.metrics.pairwise.euclidean_distances",
     "sklearn.metrics.pairwise.haversine_distances",
     "sklearn.metrics.pairwise.kernel_metrics",
     "sklearn.metrics.pairwise.laplacian_kernel",
 
@@ -227,8 +227,7 @@ def euclidean_distances(
     X, Y=None, *, Y_norm_squared=None, squared=False, X_norm_squared=None
 ):
     """
-    Considering the rows of X (and Y=X) as vectors, compute the
-    distance matrix between each pair of vectors.
+    Compute the distance matrix between each pair from a vector array X and Y.
 
     For efficiency reasons, the euclidean distance between a pair of row
     vector x and y is computed as::
@@ -250,10 +249,12 @@ def euclidean_distances(
     Parameters
     ----------
     X : {array-like, sparse matrix} of shape (n_samples_X, n_features)
+        An array where each row is a sample and each column is a feature.
 
     Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), \
             default=None
-        If `None`, uses `Y=X`.
+        An array where each row is a sample and each column is a feature.
+        If `None`, method uses `Y=X`.
 
     Y_norm_squared : array-like of shape (n_samples_Y,) or (n_samples_Y, 1) \
             or (1, n_samples_Y), default=None
@@ -270,19 +271,21 @@ def euclidean_distances(
         ``(X**2).sum(axis=1)``)
         May be ignored in some cases, see the note below.
 
-    Notes
-    -----
-    To achieve better accuracy, `X_norm_squared` and `Y_norm_squared` may be
-    unused if they are passed as ``float32``.
-
     Returns
     -------
     distances : ndarray of shape (n_samples_X, n_samples_Y)
+        Returns the distances between the row vectors of `X`
+        and the row vectors of `Y`.
 
     See Also
     --------
     paired_distances : Distances betweens pairs of elements of X and Y.
 
+    Notes
+    -----
+    To achieve a better accuracy, `X_norm_squared` and `Y_norm_squared` may be
+    unused if they are passed as `np.float32`.
+
     Examples
     --------
     >>> from sklearn.metrics.pairwise import euclidean_distances