Feature synthesis is a technique used to generate new data from existing data in order to expand limited training datasets. By combining elements from multiple samples, feature synthesis can create realistic synthetic samples that reflect the overall distribution of the original data. This allows machine learning models to be trained on larger, more varied datasets that may improve model performance compared to training only on the original limited data.