Mixed-effects models are statistical models that incorporate both fixed effects, which are consistent across individuals, and random effects, which vary between individuals or groups, making them ideal for analyzing data with hierarchical or clustered structures. These models are widely used in fields like biostatistics, social sciences, and econometrics to account for variability at multiple levels of analysis, improving the accuracy and generalizability of the results.