Feature engineering is the process of selecting, creating, or transforming data features (attributes) in a dataset to improve the performance of machine learning models. It involves identifying which aspects of the data are relevant and useful for the specific task at hand and then preparing those features in a way that makes them more informative for the model. Feature engineering is like preparing ingredients for a recipe. Just as a chef carefully selects and prepares different ingredients to make a delicious dish, in feature engineering, data scientists carefully select and create specific data attributes (features) to help machine learning models understand and predict things better.