Elastic net is a regularization technique used in machine learning and statistics to improve the performance and interpretability of predictive models, particularly in situations where there are a large number of predictor variables. It combines the penalties of both lasso (L1 regularization) and ridge regression (L2 regularization) to strike a balance between feature selection and model shrinkage. Elastic net helps to address multicollinearity and overfitting by shrinking less important variables towards zero while maintaining the grouping effect of highly correlated variables. This can result in more stable and accurate models, especially in high-dimensional datasets. Overall, elastic net is a powerful tool for building more parsimonious and robust predictive models, particularly in situations where traditional regression techniques may struggle to handle the complexity of the data.