High entropy alloys and their hydrides present an intractably large compositional design space for experiments or costly first-principles calculations like DFT, so efficient data-driven models that (1) capture key materials descriptors and (2) can predict primary thermodynamic evaluation criteria are critical. We have demonstrated how explainable machine learning models capture the dependence of different thermodynamic properties (e.g., ∆H, ∆S, or H/M) on the same critical materials features, which in turn generally produces unavoidable tradeoffs in desired hydride thermodynamic proper- ties. These include enthalpy-entropy compensation (e.g., positive correlations in ∆H vs. ∆S) as well as the capacity-enthalpy trade-off (e.g., H/M vs. ∆H) in the design of optimal materials. When high-throughput screening novel HEA hydride compositions, such trade-offs are explicitly captured and the selection of optimal materials can be re-framed as a Pareto optimal selection task. In this work we selected various Mg-containing HEAs that were predicted to be on or near the Pareto front for experimental synthesis and validation, and good agreement was obtained between ML predictions, DFT analysis, and experiments. Sacrificing capacity for decreased ∆H was unavoidable in these selected materials, which clearly highlights the need for predicting and further synthesizing Pareto efficient materials.