We present post-shrinkage strategy for the regression parameters of semiparametric models. The regression parameter vector is partitioned into two sub-vectors: the first sub-vector gives the predictors of interest, i.e., main effects (treatment effects), and the second sub-vector is for variables that may or may not needed to be controlled. We establish both theoretically and numerically that the proposed shrinkage strategy which combines two semiparametric estimators computed for the full model and the submodel outshines the full model estimation. A data example is given. We extend this strategy to high-dimensional data (HDD) analysis. For HDD analysis many penalized methods were introduced for simultaneous variable selection and parameters estimation when the model is sparse. We propose a high-dimensional shrinkage strategy to improve the prediction performance of a submodel. We demonstrate that the proposed strategy performs uniformly better than the existing methods in many cases.