Input distribution refers to the statistical properties and patterns of the data fed into a model, which can significantly impact the model's performance and generalizability. Understanding and managing input distribution is crucial for tasks like data preprocessing, feature engineering, and ensuring that training and testing datasets are representative of real-world scenarios.