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findHomography() uses RANSAC to compute a transformation matrix. It iteratively picks 4 random point pairs to compute a homography, then tests, how good it is (classifying other point pairs as inliers/outliers) and chooses the homography with the most inliers. This way it is very robust to outliers (false matches) whereas using all matches for computation would falsify the transformation.

UPDATE

Ok, here a sort of pseudo code to explain the RANSAC algorithm.

Let's say you have a list of point pairs matchlist, which represent matches between a source and a destination image. Now RANSAC does something like:

Homography bestH = null
while (termination criteria not fulfilled)
{
    randomly choose 4 matches from matchlist
    compute homography H from those 4 matches (-> not overdeterminated)

    numInliers = 0
    foreach (match m in matchlist)
    {
        if (m is inlier) numInliers += 1
    }

    if (numInliers > minNumInliers) 
    {
        if (H better than bestH) bestH = H  // H has more inliers than bestH
    }
}

This is a very simple version, the OpenCV RANSAC algorithm probably is more complex. Termination criteria typically are

maximum number of iterations -> give up, if you don't find a good homography after x iterations
minimum quality of the best homography -> don't unnecessarily perform further iterations, if you already found a good enough homography

I hope, this little example helps for better understanding.