In high-dimensional settings where number of predictors is greater than observations, many penalized methods were introduced for simultaneous variable selection and  parameters estimation when the model is sparse. However, a model may have sparse signals as well as with number predictors with weak signals. In this scenario variable selection methods may not distinguish predictors with weak signals and sparse signals. The prediction based on a selected submodel may not be preferable in such cases. For this reason, we propose a high-dimensional shrinkage strategy to improve the prediction performance of a submodel. Such a high-dimensional shrinkage estimator (HDSE) is constructed by shrinking a weighted ridge estimator in the direction of a candidate submodel. We demonstrate that the proposed HDSE performs uniformly better than the weighted ridge estimator. Interestingly, it improves the prediction performance of given submodel generated from most existing variable selection methods. The relative performance of the proposed HDSE strategy is appraised by both simulation studies and the real data analysis. Some open research problems will be discussed, as well.