In this work, we trained over 6000 ML models, including classical tabular methods (such as RF) and various graph neural networks, to investigate two research questions. First, we analyzed how distribution shifts in the training and test data affect the performance of the models. To this end, we explored seven distinct data splitting methods. Second, we investigated whether and under what conditions ID and OOD performance correlate.