Test data vs. training data
When you use machine learning algorithms on data sets, you use one part of the data (the training set) to train your algorithm (i.e., feature extraction). Once the training is completed, you'll need to evaluate the performance of the trained algorithm and you do this by applying it to new data, that is, the second part of your original data (the test data). 

With this strategy you will be able to evaluate whether your trained algorithm indeed extracts valid features of your data (i.e., it works well on the training data and on the test data), or if it was "overlearned" or "overfitted" (i.e., it performs only well on the training data and bad on the test data). 

For further information see wikipedia on training set, test set, and cross validation .