Linear Mixed Models (LMMs) are statistical models that account for both fixed and random effects, making them ideal for analyzing data with hierarchical or nested structures. They are widely used in fields like biology, psychology, and social sciences to handle repeated measures and clustered data, providing a flexible framework for modeling complex datasets with multiple sources of variation.