Ridge regression is a method used in statistics to deal with collinearity, which is when predictor variables are highly correlated. This paper introduces a new way to understand ridge regression by looking at it geometrically. The researchers show that ridge regression is like a tool that helps make the model more flexible by changing the matrix of predictor variables. By using this geometric approach, they found that adding a constant term to the main diagonal of the matrix helps improve the model's performance. They also compared different methods for dealing with collinearity and found that this new geometric approach works well in practice.