Due to the increasing amount of data, machine learning algorithms have gained importance, as they automatically process large data sets. These algorithms are calibrated by hyperparameters, that need to be chosen carefully. Hyper-parameter optimization methods are used to automatize this process. This thesis discusses hyper-parameter optimization in the context of sparse grid density estimation. First, the concept of density estimation is introduced, followed by classification and clustering, two common types of machine learning that can be based on it. Afterwards, hyper-parameters are defined, and various methods to optimize them are presented. Grid search, random search, and Bayesian optimization are discussed in theory and in the context of my implementation. These methods are used to optimize the hyper-parameters for normal and adaptive classification. Finally, the performance of the implemented methods is analyzed and compared to that of the open source software hyperopt.