GLS estimates are maximum likelihood estimates when ε follows a multivariate normal distribution with a known covariance matrix. Ridge regression[13][14][15] and other forms of penalized estimation, such as Lasso regression,[5] deliberately introduce bias into the estimation of β in order to reduce the variability of the estimate. The resulting estimates generally have lower mean squared error than the OLS estimates, particularly when multicollinearity is present or when overfitting is a problem.