Ridge Regression is a technique used in statistics and machine learning to analyze data when there are many predictors or when predictors are highly correlated. It adds a penalty to the loss function, which helps to prevent overfitting by shrinking the coefficients of the predictors. This means that even if some predictors are not very important, they won't have an outsized influence on the model. By applying this penalty, Ridge Regression ensures that the model remains stable and generalizes better to new data. It is particularly useful in situations where traditional linear regression might struggle, making it a valuable tool for data scientists and statisticians.