Out-of-Bag (OOB) is particularly useful when you want to resample data from very large datasets, as it can be a much more computationally-friendly alternative to cross-validation.

University of Michigan - Ann Arbor

Out-of-Bag (OOB) observations give way to estimating the test error of a bagged model without cross-validation. OOB means that the observations were not used to fit a given bagged tree. Using the trees that included OOB observations, it is possible to predict the response for the $i$th observation, which returns close to $B/3$ predictions. 
In the case of regression trees, these predictions can then be averaged in order to obtain a single prediction for the $i$th observation. 
With classification trees, the single prediction for the $i$th observation can be found by taking a majority vote. As a result, the OOB prediction can be used to estimate error; with regression trees, the overall OOB MSE can be found, and similarly, classification error for classification trees. 

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