Feature engineering is the practice of modifying the input data of a model to increase its prediction accuracy. This could involve selecting key variables, transforming data, or creating new variables based on domain knowledge. For example, transforming dates into day parts to capture patterns might help in a time-series model. It relates to data preprocessing as crucial preparation before model training, and is pivotal for the success of algorithms like decision trees or neural networks.