Training data refers to the labeled dataset used to train machine learning models, enabling them to learn patterns and make predictions based on input features. This data consists of input-output pairs, where the input features are the independent variables and the output labels are the dependent variables. The quality and quantity of training data significantly impact a model's performance and ability to generalize to unseen data. Effective training data should be diverse, representative, and well-annotated to cover various scenarios the model may encounter in real-world applications. Inadequate or biased training data can lead to overfitting, underfitting, or biased predictions, making the selection of training data a critical aspect of developing robust AI systems.