The increasing complexity of models used to predict, optimize and control real-world systems leads to the need for algorithms to replace complex models with far simpler surrogates for efficient computations, while preserving the accuracy of the predictions. The three-volume handbook "Model Order Reduction" covers methods as well as applications. This second volume mostly focuses on snapshot-based methods for parametric partial differential equations. This approach has seen tremendous development in the past two decades, especially in the broad domain of computational mechanics. However, the main ideas can be traced back to the 1960ies. The most popular mathematical strategy behind snapshot-based methods relies on Galerkin projection on finite-dimensional subspaces generated by snapshot solutions corresponding to a special choice of parameters. Emerging approaches for situations when the original model is not accessible for projection are based on data-driven concepts, which are also covered in one chapter of this volume. Book jacket.