title: why does LedoitWolf return zeros for off-diagonal elements?
body: <pre><code>from sklearn.covariance import EmpiricalCovariance, LedoitWolf
  
  a = np.array([[ 0.6278, -1.1273 ],
                [ 0.2323,  0.4533  ],
                [ 0.3234, 1.5356 ],
                [1.7473 ,  -0.3113 ],
                [-0.3525 , 0.2577  ]])
  
  Empirical_cov = EmpiricalCovariance().fit(a)
  Empirical_cov.covariance_
  
  array([[ 0.48009421, -0.23144627],
         [-0.23144627,  0.77341954]])
  
  LedoitWolf_cov = LedoitWolf().fit(a)
  LedoitWolf_cov.covariance_
  
  array([[ 1., -0.],
         [-0.,  1.]])
  </code></pre>
  <p>Why is <code>LedoitWolf</code> giving me zeros for the off-diagonal elements? I have a few larger datasets in which this happens.
  Is this a bug? The empirical covariance is non-zero, so shouldn't LedoitWolf also be non-zero? I read that using LedoitWolf would be slightly more accurate than empirical covariance, but what is the point if most covariance elements turn out to be zero?</p>