While studying machine learning I read about different sampling methods. Simple holdout, N-fold cross validation are straightforward. However, I somehow miss the point of bootstrapping. Its definition says that it's just a way to inflate the sample set simply by duplicating some random samples and I cannot figure what is the point in this -- seemingly no additional information in a learning process just by seeing the same instances again and again (on the contrary: others say that omitting redundant points from the training set is recommended for computational efficiency and for some other statistical reason as well).
So what is the explanation here?