The researchers developed a new method for ridge regression called robust cross validation. This method uses robust location estimators like median and least trimmed squares to improve the accuracy of the shrink parameter. By using these robust scores, the researchers found that the ridge regression model is more effective in handling outlying points. Simulations showed that the proposed estimators are successful in enhancing the robustness of the model.