Lasso Regression is a type of linear regression that helps improve the accuracy of predictions by adding a penalty for larger coefficients. This technique encourages the model to keep only the most important features, effectively reducing the number of variables used. By doing so, it helps prevent overfitting, which occurs when a model learns noise in the data rather than the underlying pattern. The name "Lasso" stands for "Least Absolute Shrinkage and Selection Operator." This method is particularly useful when dealing with datasets that have many features, as it simplifies the model while maintaining its predictive power. Overall, Lasso Regression is a valuable tool in the field of machine learning.