Shrinkage methods in statistics and machine learning refer to techniques used to reduce the complexity of a model by shrinking the coefficients of its predictors towards zero. This helps prevent overfitting and improve the model's generalization performance on new data. Some common shrinkage methods include ridge regression, lasso regression, and elastic net, which all involve adding a penalty term to the model's cost function that discourages large coefficient values. These methods are particularly useful when dealing with high-dimensional data or when the number of predictors is greater than the number of observations.