Model selection is critical to support vector learning. Previous model selection methods mainly adopt a nested two-layer framework, where the inner layer trains the learner and the outer one conducts model selection by minimizing the estimate of the generalization error. Breaking from this framework, this paper proposes an approach of simultaneously tuning multiple parameters of support vector learning, which integrates model selection and learning into one optimization process. It first combines the parameters and hyperparameters involved in support vector learning into one parameter vector. Then, using sequential unconstrained minimization technique (SUMT), it reformulates the constrained optimization problems for support vector classification (SVC) and support vector regression (SVR) as unconstrained optimization problems to give the simultaneous tuning model of SVC and SVR. In addition, it proves the basic properties of the simultaneous tuning model of SVC and SVR, including the local Lipschitz continuity and the boundedness of their level sets. Further, it develops a simultaneous tuning algorithm to iteratively solve simultaneous tuning model. Finally, it proves the convergence of the developed algorithm based on the basic properties of the simultaneous tuning model and provides analysis on complexity of the algorithm as compared with related approaches. The empirical evaluation on benchmark datasets shows that the proposed simultaneous approach has lower running time complexity and exhibits similar predictive performance as existing approaches. Theoretical and experimental results demonstrate that the simultaneous tuning approach is a sound and efficient model selection approach for support vector learning.