Data model acceleration is an efficient method to summarize data to improve search performance. It leverages precomputed summaries to speed up complex searches, making it particularly useful for large datasets. By accelerating a data model, subsequent searches on that data model are faster because the computation has already been performed and stored in an optimized format.