Ridge regression is known to shrink the coefficients of correlated predictors towards each other, allowing them to borrow strength from each other. In the extreme case of k identical predictors, they each get identical coefficients with 1/kth the size that any single one would get if fit alone. From a Bayesian point of view, the ridge penalty is ideal if there are many predictors, and all have non-zero coefficients (drawn from a Gaussian distribution).
