Generalized additive models (GAMs) are a type of statistical model that allows for more flexible relationships between variables than traditional linear regression models. They use non-monotonic functions to connect predictor variables to the response variable, giving more freedom in how the data is modeled. GAMs are more focused on the data itself rather than preconceived assumptions, making them useful for analyzing complex relationships in various fields.