Process Mining (PM) is a technique with several applications, including process discovery, which involves extracting process models from event logs that reflect how processes are actually executed. However, the resulting models are often difficult to interpret, not only due to the complexity and variability of real-world processes but also because the event logs lack sufficient information about the process. Context variables in event logs, such as resource or location, can be used to enrich the process by contextualization, making it more understandable. Selecting the appropriate level of contextual detail remains a significant challenge, as excessive contextualization can lead to complex models, while insufficient detail can obscure important insights into the process. This paper addresses this gap by proposing an entropy-based approach for context-aware process discovery, called Contextualization using Entropic Relevance (ContextER). The approach utilizes the Entropic Relevance (ER) metric to evaluate and propose contextualization that balances informativeness and complexity automatically. By evaluating the model ER, we can identify the models that optimize interpretability without hiding essential information. We demonstrate the effectiveness of the approach through a real-world case study, showing that it leads to more comprehensible and insightful process models.