Lasso regression is a statistical method used in machine learning and data analysis to enhance the accuracy of predictive models. It works by adding a penalty to the regression equation, which helps to reduce the complexity of the model. This penalty encourages the model to select only the most important features, effectively shrinking the coefficients of less relevant variables to zero. This technique is particularly useful when dealing with high-dimensional data, where many predictors may be present. By simplifying the model, Lasso regression not only improves interpretability but also helps to prevent overfitting, ensuring better performance on unseen data.