Nonparametric models are statistical methods that do not assume a specific form for the underlying data distribution. Unlike parametric models, which rely on fixed parameters, nonparametric models are flexible and can adapt to various shapes of data. This makes them useful for analyzing complex datasets where the relationship between variables is not well understood. These models often use techniques like kernel density estimation or decision trees to make predictions or identify patterns. Because they do not require strict assumptions about the data, nonparametric models can be particularly effective in fields such as machine learning and bioinformatics, where data can be highly variable.