Numerous machine learning algorithms have been developed and applied in the field. Their application indicates that there seems to be a tradeoff between their model performance and explainability. That is, machine learning models with higher performance are often based on more complex algorithms and therefore lack interpretability or explainability and vice versa. The true extent of this tradeoff remains unclear while some theoretical assumptions exist. With our research, we aim to explore this gap empirically with a user study. Using four distinct datasets, we measured the tradeoff for five common machine learning algorithms. Our two-factor factorial design considers low-stake and high-stake applications as well as classification and regression problems. Our results differ from the widespread linear assumption and indicate that the tradeoff between model performance and model explainability is much less gradual when considering end user perception. Further, we found it to be situational. Hence, theory-based recommendations cannot be generalized across applications.