Ridge's Bias Trade

Compared to ordinary least squares (OLS), how does ridge regression change the coefficient estimates when multicollinearity is present?

It guarantees unbiased estimates identical to OLS when features are correlated

It shrinks coefficients toward zero, trading some bias for reduced variance

It sets the most correlated feature's coefficient exactly to zero

It leaves coefficients unchanged but modifies only the intercept