Forward Selection is a stepwise regression technique used in statistical modeling to select a subset of predictor variables. It starts with no predictors in the model and adds them one at a time based on their statistical significance. The process continues until adding more variables does not significantly improve the model's performance. This method helps in identifying the most important variables while avoiding overfitting. By focusing on the best predictors, Forward Selection simplifies the model, making it easier to interpret and more efficient for predictions. It is commonly used in various fields, including machine learning and data analysis.