Stepwise Regression is a statistical method used to select a subset of independent variables for a regression model. It involves adding or removing predictors based on specific criteria, such as the significance of their coefficients. This process helps identify the most important variables that contribute to explaining the variation in the dependent variable. The method can be performed in two main ways: forward selection, which starts with no predictors and adds them one by one, and backward elimination, which begins with all predictors and removes them step by step. This approach aims to improve model accuracy and interpretability while avoiding overfitting.