By their very nature, imperfect-information games pose challenges of uncertainty and deception. The development of Counterfactual Regret Minimization (CFR) and its later variant CFR+, has been essential to the success of current state-of-the-art poker bots, which now exceed human performance. In this report, we implement and apply CFR and CFR+ to One-Card Poker to examine their rate of convergence. We verify the well-known result that CFR+ converges faster than CFR in One-Card Poker. We further demonstrate that CFR+ attains lower exploitability than CFR at every iteration count tested.