Forward selection is a statistical method used in model building to identify the most significant variables for predicting an outcome. It starts with no variables in the model and adds them one at a time based on their contribution to improving the model's performance. The process continues until adding more variables does not significantly enhance the model. This technique is often used in the context of regression analysis and helps simplify models by focusing on the most relevant predictors. By systematically evaluating each variable, forward selection aids in avoiding overfitting and ensures a more interpretable model.