Generalized linear models (GLMs) are a flexible extension of traditional linear regression that allow for response variables to have different distributions. They consist of three main components: a random component that specifies the distribution of the response variable, a systematic component that describes the relationship between predictors and the response, and a link function that connects the mean of the response to the linear predictors. GLMs can handle various types of data, including binary outcomes (using logistic regression), count data (using Poisson regression), and continuous outcomes with non-constant variance. This versatility makes GLMs widely applicable in fields such as statistics, biostatistics, and social sciences.