The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that make them scale-up poorly. In this paper we present a novel approach for boosting the scalability of heuristic planners based on automatically learning domain-specific search control knowledge in the form of relational decision trees. Particularly, we define the learning of planning search control as a standard classification process. Then, we use an off-the-shelf relational classifier to build domain-specific relational decision trees that capture the preferred action in the different planning contexts of a planning domain. These contexts are defined by the set of helpful actions extracted from the relaxed planning graph of a given state, the goals remaining to be achieved, and the static predicates of the planning task. Additionally, we show two methods for guiding the search of a heuristic planner with relational decision trees. The first one consists of using the resulting decision trees as an action policy. The second one consists of ordering the node evaluation of the Enforced Hill Climbing algorithm with the learned decision trees. Experiments over a variety of domains from the IPC test-benchmarks reveal that in both cases the use of the learned decision trees increase the number of problems solved together with a reduction of the time spent.