DOC: add link to the plot_mahalanobis_distances example (scikit-learn#31485)

Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>

Author: natmokval

sklearn/covariance/_empirical_covariance.py

@@ -135,7 +135,7 @@ class EmpiricalCovariance(BaseEstimator):
         Estimated location, i.e. the estimated mean.
 
     covariance_ : ndarray of shape (n_features, n_features)
-        Estimated covariance matrix
+        Estimated covariance matrix.
 
     precision_ : ndarray of shape (n_features, n_features)
         Estimated pseudo-inverse matrix.
@@ -343,6 +343,9 @@ def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True):
     def mahalanobis(self, X):
         """Compute the squared Mahalanobis distances of given observations.
 
+        For a detailed example of how outliers affects the Mahalanobis distance,
+        see :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py`.
+
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)