nn.ModuleList is just like a Python list. It was designed to store any desired number of nn.Module’s. It may be useful, for instance, if you want to design a neural network whose number of layers is passed as input:
class LinearNet(nn.Module):
  def __init__(self, input_size, num_layers, layers_size, output_size):
     super(LinearNet, self).__init__()

     self.linears = nn.ModuleList([nn.Linear(input_size, layers_size)])
     self.linears.extend([nn.Linear(layers_size, layers_size) for i in range(1, self.num_layers-1)])
     self.linears.append(nn.Linear(layers_size, output_size)

nn.Sequential allows you to build a neural net by specifying sequentially the building blocks (nn.Module’s) of that net. Here’s an example:
class Flatten(nn.Module):
  def forward(self, x):
    N, C, H, W = x.size() # read in N, C, H, W
    return x.view(N, -1)

simple_cnn = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=7, stride=2),
            nn.ReLU(inplace=True),
            Flatten(), 
            nn.Linear(5408, 10),
          )