Nonparametric models are highly flexible statistical descriptions that do not rely on strong, simplifying assumptions about the underlying data generation process. A defining common theme of nonparametric approaches is that their level of complexity grows as the amount of available training data increases. While parametric models are necessary when data is scarce to prevent overfitting, the abundance of data in modern machine learning allows for the use of fully nonparametric models that can better fit complex realities.