Despite the abundance of strategies in the literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible settings. Thus, agent designers face an "algorithm selection" problem - which negotiation strategy to choose when facing a new negotiation. Our approach to this problem is to prediet the performance of different strategies based on structural features of the domain and to select the negotiation strategy that is predicted to be most successful using a "meta-agent". This agent was able to outperform all of the finalists to the recent Automated Negotiation Agent Competition (ANAC). Our results have insights for agent-designers, demonstrating that "a little learning goes a long way", despite the inherent uncertainty associated with negotiation under incomplete information.