Probably because your original tensor was not contiguous. If resize_ changes the size of the tensor, it starts from the same point but uses a contiguous chunk of memory.
For example:
>>> m = torch.arange(0, 25).view(5, 5)
  0   1   2   3   4
  5   6   7   8   9
 10  11  12  13  14
 15  16  17  18  19
 20  21  22  23  24
[torch.FloatTensor of size 5x5]
>>> x = m[:,0]
   0
   5
  10
  15
  20
 [torch.FloatTensor of size 5])
>>> x.resize_(5, 1)
 0
 1
 2
 3
 4
[torch.FloatTensor of size 5x1]