Feature engineering is the process of selecting, transforming, or creating input features for a machine learning model to improve its performance. It involves manipulating the data to highlight important information and reduce noise, ultimately helping the model make better predictions or classifications. Examples of feature engineering include missing data handling, scaling, one-hot encoding, label encoding, and creating new features from existing ones. Effective feature engineering is often crucial for building accurate and robust machine learning models.