Stepwise selection is a statistical method used in regression analysis to select a subset of predictor variables for a model. It involves adding or removing variables based on specific criteria, such as their statistical significance or contribution to the model's predictive power. This process helps identify the most relevant variables while avoiding overfitting. The method typically includes three approaches: forward selection, where variables are added one at a time; backward elimination, where variables are removed; and bidirectional elimination, which combines both methods. Stepwise selection aims to create a simpler, more interpretable model without sacrificing accuracy.