Shrinkage is a statistical technique used to improve the estimation accuracy of parameters by introducing a penalty or constraint, often to avoid overfitting in models with many predictors. It is particularly useful in high-dimensional data settings, where traditional methods may fail to provide reliable estimates due to multicollinearity or limited sample sizes.