The article compares three methods (ridge regression, principal component regression, and partial least squares regression) to handle multicollinearity in regression analysis. Multicollinearity occurs when predictor variables are correlated with each other, which can affect the accuracy of predictions. The researchers used simulated data sets to test the performance of these methods and found that ridge regression had the lowest mean square error, making it the most effective method for handling multicollinearity.