Feature engineering is the process of selecting, transforming, or creating new features (variables) from raw data to improve the performance of machine learning models. It involves identifying the most relevant information in the data and representing it in a way that helps the model understand patterns and make more accurate predictions. Feature engineering can include tasks like scaling, one-hot encoding, imputing missing values, and generating new features through mathematical operations or domain knowledge. It plays a crucial role in optimizing the predictive power of machine learning algorithms.