This paper investigates the performance of two kernel-based surrogate models, namely Gaussian Process regression (GPR) and support vector regression (SVR), for solving uncertainty quantification (UQ) problems in aerodynamics. This research aims to shed light on both surrogate models’ approximation performance to get better insight for practical purposes. To that end, experiments using various kernel functions were performed to study their impact on GPR and SVR accuracy. Besides, the use of a composite kernel learning technique is also studied. Computational experiments show that GPR with Matern-5/2 is the most robust technique when an individual kernel is used. However, SVR with the Matern-5/2 kernel also performs better than GPR in some problems. The results suggest that there is no single best performing method when averaged over all sets of problems. Finally, we also demonstrated that using composite kernel learning, provided sufficient data samples, can further reduce the approximation error for both GPR and SVR.