Lasso regression or Least Absolute Shrinkage and Selection Operator regression is very similar to ridge regression from a conceptual point of view. Like ridge regression, it too adds a penalty for non-zero coefficients. But, while ridge regression imposes an L2 penalty (penalizing the sum of squared coefficients), lasso regression imposes an L1 penalty (penalizing the sum of their absolute values). Because of this, in lasso regression, for high values of λ, many coefficients are completely reduced to zero.