Elastic Net is a statistical method used in regression analysis that combines two techniques: Lasso and Ridge regression. It helps in selecting important variables while also addressing issues of multicollinearity, where predictor variables are highly correlated. By balancing the penalties from both methods, it can improve model performance and interpretability. This approach is particularly useful when dealing with datasets that have many predictors, especially when the number of predictors exceeds the number of observations. Elastic Net provides a more flexible solution, allowing for better generalization in predictive modeling by tuning its parameters to fit the data effectively.