Decision tree classifiers (DTs) provide an effective machine-learning model, well-known for its intuitive interpretability. However, they still miss opportunities well-established in software engineering that could further improve their explainability: separation of concerns, encapsulation, and reuse of behaviors. To enable these concepts, we introduce templates in decision diagrams (DDs) as an extension of multi-valued DDs. Templates allow to encapsulate and reuse common decision-making patterns. By a case study from the autonomous underwater robotics domain we illustrate the benefits of template DDs for modeling and explaining meta controllers, i.e., hierarchical control structures with underspecified entities. Further, we implement a template-generating refactoring method for DTs. Our evaluation on standard controller benchmarks shows that template DDs can improve explainability of controller DTs by reducing their sizes by more than one order of magnitude.