Multilevel models, also known as hierarchical models, are statistical models that account for data that is organized at more than one level, allowing for the analysis of complex data structures with nested sources of variability. These models are particularly useful in handling data with group-level effects, such as students within schools or patients within hospitals, providing insights into both individual and group-level variations.