In engineering modeling, white-box and black-box concepts represent two fundamental approaches for modeling systems. White-box models rely on detailed prior knowledge of the physical system, enabling transparent and explainable representations. Black-box models, on the other hand, consist of opaque internal workings and decision-making processes that prevent immediate interpretability. They are mainly data-driven, relying on statistical methods to capture system behavior. Depending on the literature at hand, the exact definitions of these two approaches differ. With the continuous emergence of machine learning algorithms in engineering and their move towards enhanced explainability and usability, the exact definition and assignment of white-and black-box properties soften. Grey-box modeling provides a hybrid approach. However, this term, as widely as it is used, has no clear definition either. This paper proposes a novel model on the relation of white-, black-and grey-box modeling, offering an improved categorization of conventional vanilla models, state-of-the-art hybrid models as well as the derivation of recommendations for action for targeted model improvement.