In this study, we suggest shrinkage estimation strategies based on generalized ridge regression in High-Dimensional sparse models when the design matrix is ill conditioned. The performance of the proposed estimators is compared with the usual ridge regression and some penalty estimators through Monte Carlo simulation. Finally, a real data example is given to the usefulness of the suggested estimators.