Several biased estimators have been proposed as alternatives to the Least squares estimator when multicollinearity is present in the multiple linear regression model. The ridge regression estimator and the principal components regression estimator are two techniques that have been proposed for such problems. In this paper the partitioned ridge regression estimator is developed for multiple linear regression model. This estimator commonly used to combat multicollinearity. The performance of the partitioned ridge regression estimator; in terms of covariance matrix and mean square error (MSE); is compared with partitioned least squared estimators.