Generalized Linear Models (GLMs) extend linear regression to accommodate response variables that have error distribution models other than a normal distribution, allowing for a linear relationship between the transformed expected value of the response and the predictor variables. They unify various statistical models, including linear regression, logistic regression, and Poisson regression, under a single framework by using a link function to relate the mean of the response variable to the linear predictors.