Feature engineering is the process of selecting, transforming, or creating new features from raw data to improve the performance of machine learning models. It involves extracting relevant information, dealing with missing values, scaling, encoding categorical variables, and more, to make the data more suitable for modeling. Effective feature engineering can greatly impact the accuracy and effectiveness of machine learning algorithms.