Linear regression is a common tool in epidemiology, but it can face issues when predictor variables are highly correlated. This multicollinearity can inflate the variance of the coefficient estimates, making the model unreliable. Ridge regression addresses this by adding a penalty to the size of the coefficients, thus stabilizing the estimates and improving the model's generalizability.