diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst
index a78cbe69b746d..8a2351b04ecc2 100644
--- a/doc/whats_new/v1.0.rst
+++ b/doc/whats_new/v1.0.rst
@@ -386,6 +386,10 @@ Changelog
   supporting sparse matrix and raise the appropriate error message.
   :pr:`19879` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- |Efficiency| Changed ``algorithm`` argument for :class:`cluster.KMeans` in 
+  :class:`preprocessing.KBinsDiscretizer` from ``auto`` to ``full``. 
+  :pr:`19934` by :user:`Gleb Levitskiy <GLevV>`.
+
 :mod:`sklearn.tree`
 ...................
 
diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py
index 22fa236f3314e..9ce95a97544a5 100644
--- a/sklearn/preprocessing/_discretization.py
+++ b/sklearn/preprocessing/_discretization.py
@@ -205,7 +205,8 @@ def fit(self, X, y=None):
                 init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5
 
                 # 1D k-means procedure
-                km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)
+                km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1,
+                            algorithm='full')
                 centers = km.fit(column[:, None]).cluster_centers_[:, 0]
                 # Must sort, centers may be unsorted even with sorted init
                 centers.sort()