Generalized Additive Models (GAMs) are a flexible statistical approach used to model relationships between a response variable and one or more predictor variables. They extend traditional linear models by allowing non-linear relationships through the use of smooth functions, making them useful for capturing complex patterns in data. GAMs combine the advantages of linear models and non-parametric methods, enabling analysts to interpret the effects of individual predictors while accommodating varying shapes of relationships. This makes them particularly valuable in fields like ecology, economics, and healthcare, where data often exhibit non-linear trends.