The technique of ridge regression, first proposed by Hoerl and Kennard, has become a popular tool for data analysts faced with a high degree of multicollinearity in their data. By using a ridge estimator, one hopes to both stabilize one's estimates (lower the condition number of the design matrix) and improve upon the squared error loss of the least squares estimator. Recently, much attention has been focused on the latter objective. Building on the work of Stein and others, Strawderman and Thisted have developed classes of ridge regression estimators which dominate the usual estimator in risk, and hence are minimax. The unwieldy form of the risk function, however, has led these authors to minimax conditions which are stronger than needed. In this paper, using an entirely new method of proof, we derive conditions that are necessary and sufficient for minimaxity of a large class of ridge regression estimators. The conditions derived here are very similar to those derived for minimaxity of some Stein-type estimators. We also show, however, that if one forces a ridge regression estimator to satisfy the minimax conditions, it is quite likely that the other goal of Hoerl and Kennard (stability of the estimates) cannot be realized.