Ridge regression is an alternative to least-squares estimation which is useful in overcoming problems of multicollinearity. A basic problem in ridge regression is that of choosing the biasing parameter, K, which indexes this family of estimators. We propose a method of cross validation for choosing an optimal value of K which minimizes the Predicted Residual Sum of Squares, PRESS. We have also used this method for variable selection. An example analysis is carried out.